Machine learningMachine learning
半监督朴素贝叶斯
半监督朴素贝叶斯将经典的朴素贝叶斯生成模型进行了扩展，以利用大量未标记数据和少量标记数据。它使用期望最大化（Expectation-Maximization, EM）算法，迭代地推断未标记样本的软类别分配，并重新估计类别和特征参数，从而在标记样本稀缺时获得显著更好的分类器。
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来源
- Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2–3), 103–134. DOI: 10.1023/A:1007692713085 ↗
- Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Naive Bayes (EM-augmented Generative Classifier). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-naive-bayes
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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